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1
Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction ...
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2
Could you give me a hint? Generating inference graphs for defeasible reasoning ...
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3
Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction ...
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4
Could you give me a hint ? Generating inference graphs for defeasible reasoning ...
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5
Investigating Robustness of Dialog Models to Popular Figurative Language Constructs ...
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6
Measuring and Improving Consistency in Pretrained Language Models ...
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More Identifiable yet Equally Performant Transformers for Text Classification ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.94 Abstract: Interpretability is an important aspect of the trustworthiness of a model's predictions. Transformer's predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are not explanations by proving that they are not unique. A recent study showed theoretical justifications to this observation by proving the non-identifiability of attention weights. For a given input to a head and its output, if the attention weights generated in it are unique, we call the weights identifiable. In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. Ignored in the previous works, we find the attention weights are more identifiable than we currently perceive by uncovering the hidden role of the key vector. However, the weights are still prone to ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25978-more-identifiable-yet-equally-performant-transformers-for-text-classification
https://dx.doi.org/10.48448/bkpq-tn60
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8
Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation ...
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9
Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding ...
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10
SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers ...
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11
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes ...
Jo, Yohan; Bang, Seojin; Reed, Chris. - : arXiv, 2021
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12
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer ...
Lyu, Yiwei; Liang, Paul Pu; Pham, Hai. - : arXiv, 2021
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13
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer ...
NAACL 2021 2021; Hovy, Eduard; Liang, Paul Pu. - : Underline Science Inc., 2021
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14
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes ...
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15
Extracting Implicitly Asserted Propositions in Argumentation ...
Jo, Yohan; Visser, Jacky; Reed, Chris. - : arXiv, 2020
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16
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? ...
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17
Measuring Forecasting Skill from Text ...
Zong, Shi; Ritter, Alan; Hovy, Eduard. - : arXiv, 2020
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18
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT ...
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19
On aligning OpenIE extractions with Knowledge Bases: A case study
Gashteovski, Kiril; Gemulla, Rainer; Kotnis, Bhushan. - : Association for Computational Linguistics (ACL), 2020
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20
Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
In: Computational Linguistics, Vol 45, Iss 4, Pp 627-665 (2020) (2020)
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